cubnm.datasets

Example datasets

load_sc(what, parc[, return_path])

Load example structural connectivity matrix

load_functional(what, parc[, exc_interhemispheric, ...])

Load example lower triangle of FC/FCD

load_maps(names, parc[, norm])

Loads example heterogeneity maps
cubnm.datasets.load_sc(what, parc, return_path=False)

Load example structural connectivity matrix

Parameters

what{‘strength’, ‘length’}

  • ‘strength’: SC strength (normalized tract counts)

  • ‘length’: SC tracts length

parc{‘schaefer-[100, 200, 400, 600]’, ‘aparc’, ‘glasser-360’}

parcellation. For Schaefer, specify number of parcels.

return_pathbool, optional

If True, returns path to the file Otherwise, returns the matrix

Returns

np.ndarray or str

Structural connectivity matrix or path to its text file. Shape: (nodes, nodes)

cubnm.datasets.load_functional(what, parc, exc_interhemispheric=True, return_path=False)

Load example lower triangle of FC/FCD

Parameters

what: {‘FC’, ‘FCD’}

  • ‘FC’: functional connectivity

  • ‘FCD’: functional connectivity dynamics

parc: ‘schaefer-100’

parcellation

exc_interhemispheric: bool, optional

whether to exclude interhemispheric connections

return_pathbool, optional

If True, returns path to the file Otherwise, returns the matrix

Returns

np.ndarray or str

Lower triangle of FC/FCD matrix or path to its text file. Shape: (n_pairs,)

cubnm.datasets.load_maps(names, parc, norm='minmax')

Loads example heterogeneity maps

Parameters

names: str or list

One or more maps selected from this list: - ‘myelinmap’ - ‘thickness’ - ‘fcgradient01’ - ‘genepc1’ - ‘nmda’ - ‘gabaa’ - ‘yeo7’

parc: {‘schaefer-100’}

parcellation

norm: {‘zscore’, ‘minmax’, None}

  • ‘zscore’: maps are z-score normalized

  • ‘minmax’: maps are min-max normalized to [0, 1]

return_pathbool, optional

If True, returns path to the file Otherwise, returns the matrix

Returns

np.ndarray or str

Maps arrays or path to their text file. Shape: (maps, nodes)

Notes

For more information and code on how these maps were obtained and parcellated see utils.datasets.load_maps in https://github.com/amnsbr/eidev. The set of maps included here are limited and provided just as examples. We recommend users to use neuromaps and similar tools to obtain and parcellate further maps.